5 Examples of Artificial Intelligence in Finance

Artificial intelligence was a long shot for many businesses because it was too complex, expensive and most importantly exclusive. But with technologies like Generative AI, AI is becoming remarkably accessible and scalable even for modest-sized financial institutions. The key is pre-built models that can be easily customised for specific business needs instead of building from scratch.

Whether you're a CEO looking to detect fraud earlier a tax leader aiming to automate audits, a bank manager hoping to improve lending decisions or a financial advisor seeking more personalised client recommendations, AI has become integral across diverse roles. Reports show that 41% of financial services executives believe AI-powered chatbots will have the most impact on their industry in the coming year. However, chatbots represent only a fraction of the transformative AI solutions financial institutions are adopting.

Importance of AI in Finance

If your end goal is clear, which most probably would be providing superior quality to clients and building long-lasting customer relationships then Artificial Intelligence is for you. Adopting AI solutions allows you to remain competitive, control costs, manage risks and generate growth.

Implementing AI can optimise efficiency by taking over routine analytical and operational tasks, freeing your staff to focus more on strategic, high-value responsibilities that emphasise service, relationships and revenue expansion. It’s fascinating how AI algorithms can work 24/7 to handle everything from basic customer queries and fraud monitoring to complex options trading strategies and customised portfolio management. This improves consistency and continuously leverages your latest data and insights while liberating human teams for judgment-intensive work that builds trust and loyalty.

One of the best parts about employing AI in any business, finance in this case is personalisation. You can leverage personalised offerings to individual client needs in real time. Hyper-specific recommendations and advice on an individual level promote customer satisfaction, cross-selling, upselling and retention for your business. 

With business, comes risk. AI in Finance can also improve risk analytics to spot anomalies earlier and respond to market changes faster while generating predictive business intelligence you can act on with confidence to drive growth.

Common Examples of AI in Finance

AI is already transforming a diverse range of capabilities across the financial sector, including:

• Risk Management: AI models identify historical market data, risk indicators, economic variables, and investment portfolios to construct accurate risk assessments. Quantitative analysis allows generators of additional scenarios for stress testing. This strengthens risk mitigation strategies.

• Fraud Detection: AI fraud systems employ advanced anomaly detection techniques across transactions, claims assessments and identity verifications. Complex pattern recognition provides reliable fraud flags in real-time by screening massive databases beyond human capability. Minimising such financial crime saves costs.

• Automation: AI streamlines inefficient manual tasks in financial institutions through intelligent process automation. Handling routine data entry, report generation, calculations and record-keeping without human involvement boosts productivity and reduces labour expenditure.

• Sentiment Analysis: AI performs contextual mining of customer surveys, call transcripts, social posts etc. to gauge satisfaction and emotional tone. Identifying complaints, frustrations and positives provides actionable insights for service enhancements targeted to areas that need improvement.

• Anomaly Detection: AI spots deviations from normative models constructed from historical data. Unusual account activity, fraudulent insurance claims and atypical credit patterns are flagged through outlier detection capabilities for further examination. Early anomaly warnings limit financial risk.

• Recommendations: Based on financial profiles, past behaviour, economic trends and market simulations, AI suggests customised investment options, loan products, or insurance policies it calculates as best fit to what a specific customer may desire or require. This increases cross-sell potential.

• Translation: AI translation tools powered by neural networks allow real-time communication between financial institution staff and clients speaking different languages with higher accuracy. Enabling multilingual interactions expands business reach and revenue.

• Document Processing: Intelligent document processing solutions rely on AI for structured information extraction and data categorization from forms, bills, invoices, contracts etc. Automating these workflows saves costs and bolsters analytics capabilities.

• Image Recognition: AI achieves high accuracy in assessing image-based insurance claims for damage analysis and fraud through computer vision techniques. Improved assessment efficiency also applies to cheque processing, signature verification, and document object detection.

5 Best Examples of AI in Finance

Here are 5 compelling ways through which leading financial institutions are leveraging the potential of AI:

Anti-Money Laundering Monitoring

AI systems perform customer due diligence, transaction monitoring and suspicious activity reporting to detect financial crimes. Cloud GPUs for AI can flag abnormal patterns and suspicious activities faster, improving detection rates and reducing false positives. This acceleration streamlines AML processes, allowing financial institutions to gain efficiency over manual review alone, while also enhancing compliance with regulatory requirements.

Algorithmic Trading

AI stock and commodities trading algorithms use vast datasets and quantitative analysis to make or recommend investment decisions. With this, you can quickly identify profitable opportunities, execute precisely timed trades, minimise tax impacts and enable customisation at scale. Self-learning algorithms continuously ingest diverse data like prices, news, SEC filings, and sentiment to recognise predictive patterns, model market dynamics, optimise trade timing/sizing, and implement quantitative strategies. By leveraging GPUs for parallel computing, self-learning algorithms can ingest diverse market data and identify predictive patterns more swiftly.

Automated Credit Scoring

AI algorithms analyse diverse data points to make real-time loan approval decisions. While analysing vast amounts of data for credit scoring, GPUs can expedite computations, enabling quicker decision-making on loan approvals. With their parallel processing capabilities, GPUs enhance the speed of machine learning models, allowing financial institutions to process historical loan performance data and alternative credit information more rapidly. This acceleration ensures that credit decisions are made swiftly, reducing default risk exposure and expanding access to credit for more applicants.

AI-Enhanced Cybersecurity

Leveraging GPU-accelerated deep learning algorithms, AI systems can detect anomalies and zero-day malware more rapidly. This acceleration enables real-time defence against cyber threats, reducing breach impacts and enhancing fraud coverage, while also overcoming challenges related to volume and false positives in threat detection.

Chatbots for Customer Service

When it comes to AI-powered chatbots for customer service, GPUs can enhance the responsiveness and scalability of virtual assistants. By leveraging GPU-accelerated natural language processing, chatbots can interpret customer queries and access account data more quickly. This acceleration enables chatbots to provide 24/7 self-service access, reduce call volumes, and improve response consistency, improving the customer experience while cutting support costs.

Also Read: How GPUs Supercharge AI and ML for Breakthroughs

Future of AI in Finance

The future of Artificial Intelligence in finance looks promising, with the global Artificial Intelligence in Finance market projected to grow substantially over the next decade. According to recent reports, the market size was valued at USD 150.2 billion in 2023 and is estimated to reach USD 1,345 billion by 2030, growing at an impressive CAGR of 36% during the forecast period.

This rapid expansion is attributed to the increased use of AI technologies in banking, insurance, trading, and other sectors of finance. For instance, AI-powered chatbots have been adopted widely to handle customer queries, while machine learning algorithms are used extensively for fraud detection and credit scoring. Natural language processing (NLP) is also being integrated into financial platforms to facilitate human-machine interaction which is fascinating. 

However, despite these positive developments, there remain some challenges associated with implementing AI in finance. Data quality remains a major concern, given the large volumes of data generated daily in the financial sector. Regulatory compliance could be another prevalent issue, particularly when dealing with sensitive information like client data. The good thing is that these challenges present opportunities for companies to innovate and develop better AI tools to overcome them.

Conclusion

To accelerate AI workloads in finance, leveraging cloud GPUs can significantly boost performance and scalability. Cloud GPUs offer the computational power needed to process vast amounts of data efficiently and enable complex AI algorithms to run seamlessly. With cost-effective cloud GPU solutions at Hyperstack, you can access cutting-edge GPU technology to stay ahead in the industry. 

We offer a variety of NVIDIA GPUs, specifically designed for AI applications in finance, including the NVIDIA A100, H100 and many others. These GPUs cater to diverse performance and budget needs. For example, the NVIDIA A100 GPU is built for high-speed workloads, making it perfect for handling complex AI tasks in finance. It boasts exceptional memory bandwidth of 80GB and world-record 2TB/s bandwidth to tackle massive datasets and complex models.

Similarly, the NVIDIA H100 SXM and the NVIDIA H100 PCIe are ideal for the most intensive AI and HPC applications that demand peak computational power. For example, the NVIDIA H100 PCIe is specifically designed for the deployment of large-scale language models such as Chat GPT that can assist the finance industry by providing instant access to vast amounts of financial knowledge, market insights, and regulatory information. It can aid in automating customer support through AI-driven chatbots, handling routine inquiries, account management, and dispute resolution. 

Sign up for a free Hyperstack account today and experience the power of AI in Finance with the right GPU partner by your side!

FAQs

What are some examples of AI in Finance?

Some examples of AI in finance include risk management, fraud detection, process automation, sentiment analysis, anomaly detection, investment recommendations, translation services, document processing, and image recognition.

What are the benefits of using AI in Finance?

The main benefits of using AI in finance are improved efficiency through automation of routine tasks, enhanced risk management and fraud detection, personalisation of offerings to customers accelerated growth through data-driven insights and recommendations, and better compliance with regulations.

What are the challenges of implementing AI in Finance?

Key challenges of implementing AI in finance include poor data quality, high costs of developing customised AI solutions, integration difficulties with legacy systems, lack of in-house expertise, explaining AI-driven decisions for transparency, and keeping up with evolving regulations around the use of customer data and algorithms.

Which is the best GPU for AI in Finance?

Some of the best GPUs for accelerating AI in finance workloads are the NVIDIA A100 for high-speed processing, the H100 SXM5 for peak computational power, and the H100 PCIe which is optimised for large-scale language models that can enhance customer service and business insights.